An overview of how organizations are using artificial intelligence to move from reactive testing to proactive quality engineering—reducing costs, accelerating releases, and improving reliability. Topics include automating and optimizing test design for higher coverage, predicting defects and focusing testing where it matters, enabling intelligent test automation with self-healing scripts, continuously monitoring quality across CI/CD pipelines, and generating realistic, privacy‑compliant test data. Expected outcomes discussed include reducing total cost of quality by up to 25% and accelerating release cycles by 20%.
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Chris LaPoint
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Vice President of AI Growth Acceleration
Tricentis
Chris LaPoint is the Vice President of AI Growth Acceleration at Tricentis, where he helps organizations adopt AI-powered testing to reduce the cost of quality, accelerate releases, and increase customer trust. Chris leads the Rapid Innovation Program Team (RIPT), Tricentis' initiative for advancing agentic AI development. Through RIPT, product managers work directly with customers and partners to test early versions of Tricentis agentic solutions in real-world environments and iterate rapidly based on results. With over 20 years of experience in product strategy, growth, and user experience at companies like GoDaddy, SolarWinds, and SpareFoot, Chris has a proven track record of delivering solutions that improve software quality, speed, and business outcomes. Chris holds a B.S. in Electrical and Computer Engineering from The University of Texas at Austin and is passionate about connecting technology innovation to tangible customer results.
Bio from: The Cost Of Quality
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The Cost of Quality (CoQ) has long been a challenge for QA teams; where rework, production incidents, and defect leakage silently erode profitability. In traditional environments, up to 70% of CoQ comes from failure-related activities. But with AI-driven testing, that balance is shifting. This webinar explores how organizations are using artificial intelligence to move from reactive testing to proactive quality engineering—reducing costs, accelerating releases, and improving reliability. Join us to uncover how AI can:
- Automate and optimize test case design for higher coverage and reduced effort.
- Predict defects and focus testing where it matters most.
- Enable intelligent test automation with self-healing scripts.
- Continuously monitor quality across CI/CD pipelines.
- Generate realistic, privacy-compliant test data with minimal manual effort.
We’ll also discuss measurable business outcomes—like reducing total CoQ by up to 25% and accelerating release cycles by 20%.
Learn how to turn testing from a cost center into a strategic value driver by engineering quality from the start.